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적응적인 Saliency Map 모델 구현
Implementation of Image Adaptive Map

박상범   (숭실대학교 전자공학과UU0000851  ); 김기중   (숭실대학교 전자공학과UU0000851  ); 한영준   (숭실대학교 전자공학과UU0000851  ); 한헌수   (숭실대학교 전자공학과UU0000851  );
  • 초록

    This paper presents a new saliency map which is constructed by providing dynamic weights on individual features in an input image to search ROI(Region Of Interest) or FOA(Focus Of Attention). To construct a saliency map on there is no a priori information, three feature-maps are constructed first which emphasize orientation, color, and intensity of individual pixels, respectively. From feature-maps, conspicuity maps are generated by using the It's algorithm and their information quantities are measured in terms of entropy. Final saliency map is constructed by summing the conspicuity maps weighted with their individual entropies. The prominency of the proposed algorithm has been proved by showing that the ROIs detected by the proposed algorithm in ten different images are similar with those selected by one-hundred person's naked eyes.


  • 주제어

    현저한 지도 .   특징 지도 .   현격함 지도 .   관심 영역 .   관심 집중.  

  • 참고문헌 (14)

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    3. Kim, T. H. and Jeong, D. S., 'An Image Retrieval Technique using Entropy and Color Features,' The Korean Institute of Information Scientists and Engineers(KIISE), Vol. 26, No. 3, pp. 282-290, 1999 
    4. Ouerhani, N. and Hugli, H., 'MAPS: Multiscale Attention-Based PreSegmentation of Color Images,' Lecture Notes in Computer Science, Vol. 2695, pp. 537-549, 2003 
    5. Itti, L., Koch, C. and Niebur, E., 'Model of saliency-based visual attention for rapid scene analysis,' IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), pp. 1254-1259, 1998 
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    8. Choi, K. J. and Lee, Y. B., 'Detecting Salient Regions based on Bottom - up Human Visual Attention Characteristic,' The Korean Institute of Information Scientists and Engineers(KIISE) : Software and application, Vol. 31, No. 2, pp. 189-202, 2004     
    9. Park, M. C. and Cheoi, K. J., 'A Motion - driven Selective Visual Attention System,' The Korea Contents Association, Vol. 5, No. 6, pp. 87-96, 2005 
    10. Milanese, R., Wechsler, H., Gil, S., Bost, J. and Pun, T., 'Integration of Bottom-up and Top-down Cues for Visual Attention Using Non-Linear Relaxation,' Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 781-785, 1994 
    11. Lee, T. W., Wachtler, T. and Sejnowski, T. J., 'Color Opponency is an Efficient Representation of Spectral Properties in Natural Scenes,' Vision Research, Vol. 42, Issue 17, pp. 2095-2103, 2002 
    12. Hu, Y., Xie, X., Ma, W. Y., Chia, L. T. and Rajan, D., 'Salient Region Detection Using Weighted Feature Maps Based on the Human Visual Attention Model,' Lecture Notes in Computer Science, Vol. 3332, pp. 993-1000, 2004 
    13. Ouerhani, N., Bur, A. and Hugli, H., 'Linear vs. Nonlinear Feature Combination for Saliency Computation: A Comparison with Human Vision,' Lecture Notes in Computer Science, Vol. 4174, pp. 314-323, 2006 
    14. Park, S. J., Shin, J. K. and Lee, M. H., 'Biologically Inspired Saliency Map Model for Bottom-up Visual Attention,' Lecture Notes in Computer Science, Vol. 2525, pp. 418-426, 2002 

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